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End of training

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README.md ADDED
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+ ---
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+ license: mit
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - xglue
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: xlm-v-base-finetuned-xglue-xnli
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: xglue
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+ type: xglue
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+ config: xnli
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+ split: validation.en+validation.ar+validation.bg+validation.de+validation.el+validation.es+validation.fr+validation.hi+validation.ru+validation.sw+validation.th+validation.tr+validation.ur+validation.vi+validation.zh
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+ args: xnli
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.7402677376171352
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # xlm-v-base-finetuned-xglue-xnli
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+
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+ This model is a fine-tuned version of [stefan-it/xlm-v-base](https://huggingface.co/stefan-it/xlm-v-base) on the xglue dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6511
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+ - Accuracy: 0.7403
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 2
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 1.0994 | 0.08 | 1000 | 1.0966 | 0.3697 |
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+ | 1.0221 | 0.16 | 2000 | 1.0765 | 0.4560 |
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+ | 0.8437 | 0.24 | 3000 | 0.8472 | 0.6179 |
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+ | 0.6997 | 0.33 | 4000 | 0.7650 | 0.6804 |
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+ | 0.6304 | 0.41 | 5000 | 0.7227 | 0.7007 |
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+ | 0.5972 | 0.49 | 6000 | 0.7430 | 0.6977 |
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+ | 0.5886 | 0.57 | 7000 | 0.7365 | 0.7066 |
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+ | 0.5585 | 0.65 | 8000 | 0.6819 | 0.7223 |
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+ | 0.5464 | 0.73 | 9000 | 0.7222 | 0.7046 |
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+ | 0.5289 | 0.81 | 10000 | 0.7290 | 0.7054 |
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+ | 0.5298 | 0.9 | 11000 | 0.6824 | 0.7221 |
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+ | 0.5241 | 0.98 | 12000 | 0.6650 | 0.7268 |
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+ | 0.4806 | 1.06 | 13000 | 0.6861 | 0.7308 |
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+ | 0.4715 | 1.14 | 14000 | 0.6619 | 0.7304 |
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+ | 0.4645 | 1.22 | 15000 | 0.6656 | 0.7284 |
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+ | 0.4443 | 1.3 | 16000 | 0.7026 | 0.7270 |
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+ | 0.4582 | 1.39 | 17000 | 0.7055 | 0.7225 |
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+ | 0.4456 | 1.47 | 18000 | 0.6592 | 0.7361 |
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+ | 0.44 | 1.55 | 19000 | 0.6816 | 0.7329 |
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+ | 0.4419 | 1.63 | 20000 | 0.6772 | 0.7357 |
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+ | 0.4403 | 1.71 | 21000 | 0.6745 | 0.7319 |
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+ | 0.4348 | 1.79 | 22000 | 0.6678 | 0.7338 |
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+ | 0.4355 | 1.87 | 23000 | 0.6614 | 0.7365 |
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+ | 0.4295 | 1.96 | 24000 | 0.6511 | 0.7403 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.0
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+ - Pytorch 1.13.1+cu116
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+ - Datasets 2.9.0
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+ - Tokenizers 0.13.2
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